Appreciating symptoms and deficits in schizophrenia: Right posterior insula and poor insight

Palaniyappan, Lena; Mallikarjun, Pavan; Joseph, Verghese; Liddle, Peter F.

doi:10.1016/j.pnpbp.2010.12.008

Cited by 49 publications

(33 citation statements)

References 56 publications

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“…We found several brain measures that differed between groups and that have been described in other SCZ morphometric studies394041424344. The right cerebellar white matter demonstrated the greatest difference between groups.…”

Section: Discussionsupporting

confidence: 73%

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Pinaya

Gadelha

Doyle

et al. 2016

Sci Rep

142

114

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Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.

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Section: Discussionsupporting

confidence: 73%

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Pinaya

Gadelha

Doyle

et al. 2016

Sci Rep

142

114

View full text Add to dashboard Cite

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“…40 Indeed, the right insula in particular has been linked to awareness deficits related to limb ownership and limb functioning in the context of anosognosia for hemiplegia 41 as well as degree of insight in patients with schizophrenia. 42 Craig (2009) has argued that this brain region provides a central resource for processing and representing subjective states across time, and that a spatial gradient exists such that the posterior insular cortex is specialized for re-representing primary interoceptive experiences (e.g., heart rate, breathing, temperature) while the anterior insula is engaged in relation to awareness of higher order cognitive and emotional experiences. 43 …”

Section: Discussionmentioning

confidence: 99%

The right insula contributes to memory awareness in cognitively diverse older adults

et al. 2015

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Unawareness of memory loss is a challenging characteristic of Alzheimer’s disease (AD) and other age-related neurodegenerative conditions at their earliest stages, adversely affecting important outcomes such as patient decision making and safety. The basis of this metacognitive disturbance has been elusive; however it is almost certainly determined in part by compromise to brain regions critical for self-assessment. The subjectivity of traditional measurements of self-awareness in dementia has likely limited the rigor with which its neuroanatomic correlates can be established. Here we objectively measure memory awareness (metamemory) using a Feeling of Knowing (FOK) task in a group of cognitively diverse older adults, including 14 with mild AD and 20 cognitively healthy older adults. Performance on the metamemory task was examined in relation to the structural integrity of 14 bilateral neuroanatomic regions hypothesized to support self-awareness. Less accurate metamemory was associated only with reduced right insular volume (r = .41, p = .019). Implications of the current findings for models of metacognitive aging are discussed, with attention to the role of the insula in the conscious detection of errors.

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“…Posterior insular cortex has been implicated in agency, interoceptive awareness, and a sense of self (Craig, 2002; Farrer et al, 2003) and evidence has accumulated that interoceptive awareness and emotional experience are strongly linked (e.g., Damasio, 1996). One study of patients with schizophrenia found that cortical surface area and white matter volume of the right posterior insula were negatively related to insight (Palaniyappan et al, 2011). Although in the present study we measured gray matter volumes and not cortical surface area or white matter volumes, it is possible that the observed gray matter deficits in the homicide offenders are related to deficient emotional states involving insight, agency, appreciation of the interpersonal harm and seriousness of one's actions and, in turn, violent homicidal behavior.…”

Section: Discussionmentioning

confidence: 99%

Abnormal brain structure in youth who commit homicide

Cope

Ermer

Gaudet

et al. 2014

NeuroImage: Clinical

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BackgroundViolence that leads to homicide results in an extreme financial and emotional burden on society. Juveniles who commit homicide are often tried in adult court and typically spend the majority of their lives in prison. Despite the enormous costs associated with homicidal behavior, there have been no serious neuroscientific studies examining youth who commit homicide.MethodsHere we use neuroimaging and voxel-based morphometry to examine brain gray matter in incarcerated male adolescents who committed homicide (n = 20) compared with incarcerated offenders who did not commit homicide (n = 135). Two additional control groups were used to understand further the nature of gray matter differences: incarcerated offenders who did not commit homicide matched on important demographic and psychometric variables (n = 20) and healthy participants from the community (n = 21).ResultsCompared with incarcerated adolescents who did not commit homicide (n = 135), incarcerated homicide offenders had reduced gray matter volumes in the medial and lateral temporal lobes, including the hippocampus and posterior insula. Feature selection and support vector machine learning classified offenders into the homicide and non-homicide groups with 81% overall accuracy.ConclusionsOur results indicate that brain structural differences may help identify those at the highest risk for committing serious violent offenses.

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Appreciating symptoms and deficits in schizophrenia: Right posterior insula and poor insight

Cited by 49 publications

References 56 publications

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

The right insula contributes to memory awareness in cognitively diverse older adults

Abnormal brain structure in youth who commit homicide

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